Comparisons between miRNAs from fresh and biobanked serum.

<p>A) Fractions of small RNA identified in both fresh (-80°C/<1 year storage) and biobanked (-20^°C/>10 year storage) according to category using Bowtie mapping. UMD3.1 denotes mappings to the reference genome. miRNA fractions are shown inset in detail. Note these values slightly underestimate the total miRNAs present since they only include miRBase hairpins and not novel content. B) Overlap between fresh (-80°C/<1 year storage) and biobanked (-20°C/>10 year storage) serum core miRNAs and a bovine macrophage dataset. C) Comparison of top 8 miRNAs in fresh (-80°C/<1 year storage) and biobanked (-20°C/>10 year storage) samples shows large differences in the top 2 most abundant miRNAs. Counts are the means of normalised value per sample and error bars show the standard deviation over all n samples. For fresh samples n = 24, for biobanked samples n = 57. D) Correlation between log mean normalised counts for the same miRNA in fresh (-80°C/<1 year storage) and biobanked (-20°C/>10 year storage) datasets. Error bars indicate the variance over all samples of mean read count.</p

Normalised read variation for differentially abundant miRNA over 5 time points.

<p>Normalised reads are shown for distinct miRNA at the 0, 6, 43, 46 and 46-month time points. Note the x axis time interval is not to scale.</p

IsomiR abundances and variant representation in different datasets.

<p>A) IsomiR normalised counts compared between 1) fresh (-80°C/<1 year storage) and biobanked (-20°C/>10 year storage) bovine serum, 2) serum and macrophage (both bovine) and 3) bovine and human serum. B) Classification scheme for isomiRs using sRNAbench is hierarchical. <i>exactNucVar</i> means single nucleotide changes to the canonical sequence, most probably due to sequencing errors. <i>mv</i> indicates shifted sequences. non-templated addition is enzymatically addition of a nucleotide to the 3’ end and is given priority by sRNAbench since these changes may be of biological relevance. C) Plot shows the counts of dominant isomiRs categorised by class. The general trend of dominance is the same across all datasets, including non-serum.</p

isomiR abundance patterns compared between fresh and biobanked serum.

<p>A) Relative abundances of isomiRs with a single dominant form in the fresh (-80°C/<1 year storage) dataset but not in the biobanked (-20°C/>10 year storage) data. Note that many of the low abundance forms are not present in the biobanked data (green bars). B) Relative abundances (shown as fraction of total) of some miRNAs with multiple sub-dominant isomiRs compared between fresh and biobanked year samples.</p

Canonical and dominant isomiR differences.

<p>A) bta-miR-22-3p total abundance profile over time (left) and for 2 isomiRs (right), both are shorter 3’ variants. Analysing these isomiRs individually allows them to be detected as being differentially expressed over time. B) bta-miR-22-3p isomiR relative abundances. The canonical form, labelled <i>exact</i>, is only fourth most abundant. C) The canonical and dominant isomiRs differ in >50% of cases. Shown in the plot are the corresponding percentages between the 2 isoforms, when different. (By definition <i>dominant</i> means highest percentage of reads). For points in the top left of the plots, the actual canonical form is insignificant. Note the lower number of points for the 10–15 year (CVI) dataset.</p

Monitoring of the MAP experimental infection.

<p><b>(A)</b> Detection of anti-MAP antibodies in serum via ELISA across 49 months of the time-course. Six individual experimentally infected cattle (in red A-F) and six individual naturally infected cattle (in green G-L) are represented. Faecal culture data for <b>(B)</b> experimentally infected and <b>(C)</b> naturally infected animals at monthly intervals. 0 = negative, 1 = 1 cfu/ agar slant, 2 = <50 cfu/agar slant, 3 = <100 cfu/agar slant and 4 = >100 cfu agar slant. C = fungal contamination, resulting in an inability to accurately determine MAP cfus.</p

miRNA abundances in miRDeep2 and sRNAbench.

<p>A: Proportion of reads mapped to different small RNAs. Values are the average percentage over all samples. Reads matching tRNA dominate. The unmapped percentage is likely due to mismatches in the reads or unannotated ncRNAs. B: Variation of known miRNA discovered with increasing random reads for both methods used in this study. sRNAbench is more sensitive to low abundance genes and produces more hits at a given file size. miRDeep2 was run with no score cut-off. Discovery tails off at 4 million reads in both methods. The inset plot shows the mean abundance of each newly discovered set, illustrating that only low abundance miRNAs are being added after ~3 million mark. C: Overlap between the top 80 miRDeep and sRNAbench results for known miRNAs shows almost identical results. D: Correlation between total read counts determined by both methods for the overlapping miRNAs.</p

Top 20 dominant isomiRs across all samples.

<p>Those not marked as 'exact' variants are different from the canonical miRBase sequence. Variants are named using the sRNAbench nomenclature.</p

Novel micro RNAs predicted in the data by mirDeep2 (after filtering steps).

<p>The 'gene' column denotes a host gene where found with transcription unit (intron or exon). The table is sorted by conservation in mammal species, that is, the number of species with aligned orthologs (aligned column). tr. unit = transcription unit. *max identity is the highest sequence identity to another species.</p

The Identification of Circulating MiRNA in Bovine Serum and Their Potential as Novel Biomarkers of Early <i>Mycobacterium avium</i> subsp <i>paratuberculosis</i> Infection

<div><p><i>Mycobacterium avium</i> subspecies <i>paratuberculosis</i> (MAP) is the aetiological agent of Johne’s disease (JD), a chronic enteritis in ruminants that causes substantial economic loses to agriculture worldwide. Current diagnostic assays are hampered by low sensitivity and specificity that seriously complicate disease control; a new generation of diagnostic and prognostic assays are therefore urgently needed. Circulating microRNAs (miRNAs) have been shown to have significant potential as novel biomarkers for a range of human diseases, but their potential application in the veterinary sphere has been less well characterised. The aim of this study was therefore to apply RNA-sequencing approaches to serum from an experimental JD infection model as a route to identify novel diagnostic and prognostic miRNA biomarkers. Sera from experimental MAP-challenged calves (n = 6) and age-matched controls (n = 6) were used. We identified a subset of known miRNAs from bovine serum across all samples, with approximately 90 being at potentially functional abundance levels. The majority of known bovine miRNAs displayed multiple isomiRs that differed from the canonical sequences. Thirty novel miRNAs were identified after filtering and were found within sera from all animals tested. No significant differential miRNA expression was detected when comparing sera from MAP-challenged animals to their age-matched controls at six-month’s post-infection. However, comparing sera from pre-infection bleeds to six-month’s post-infection across all 12 animals did identify increased miR-205 (2-fold) and decreased miR-432 (2-fold) within both challenged and control groups, which suggests changes in circulating miRNA profiles due to ageing or development (P<0.00001). In conclusion our study has identified a range of novel miRNA in bovine serum, and shown the utility of small RNA sequencing approaches to explore the potential of miRNA as novel biomarkers for infectious disease in cattle.</p></div